Raman Tweezers for Tire and Road Wear Micro- and Nanoparticles Analysis
Pietro Giuseppe Gucciardi, Gillibert Raymond, Alessandro Magazzù, Agnese Callegari, David Brente Ciriza, Foti Antonino, Maria Grazia Donato, Onofrio M. Maragò, Giovanni Volpe, Marc Lamy de La Chapelle & Fabienne Lagarde
Tire and Road Wear Particles (TRWP) are non-exhaust particulate matter generated by road transport means during the mechanical abrasion of tires, brakes and roads. TRWP accumulate on the roadsides and are transported into the aquatic ecosystem during stormwater runoffs. Due to their size (sub-millimetric) and rubber content (elastomers), TRWP are considered microplastics (MPs). While the amount of the MPs polluting the water ecosystem with sizes from ~ 5 μm to more than 100 μm is known, the fraction of smaller particles is unknown due to the technological gap in the detection and analysis of < 5 μm MPs. Here we show that Raman Tweezers, a combination of optical tweezers and Raman spectroscopy, can be used to trap and chemically analyze individual TWRPs in a liquid environment, down to the sub-micrometric scale. Using tire particles mechanically grinded from aged car tires in water solutions, we show that it is possible to optically trap individual sub-micron particles, in a so-called 2D trapping configuration, and acquire their Raman spectrum in few tens of seconds. The analysis is then extended to samples collected from a brake test platform, where we highlight the presence of sub-micrometric agglomerates of rubber and brake debris, thanks to the presence of additional spectral features other than carbon. Our results show the potential of Raman Tweezers in environmental pollution analysis and highlight the formation of nanosized TRWP during wear.
Deep learning for microscopy, optical trapping, and active matter
(online at) TU-Darmstadt, Germany
18 June 2021, 14:00 CEST
After a brief overview of artificial intelligence, machine learning and deep learning, I will present a series of recent works in which we have employed deep learning for applications in photonics and active matter.
In particular, I will explain how we employed deep learning to enhance digital video microscopy, to estimate the properties of anomalous diffusion, to characterize microscopic force fields, to improve the calculation of optical forces, and to characterize nanoparticles.
Finally, I will provide an outlook for the application of deep learning in photonics and active matter.
The disputation took place at 9 a.m. digitally via Zoom. A link to the Zoom meeting was published the day before dissertation on the GU website.
Title: Deep Learning Applications – From image analysis to medical diagnosis
Deep learning is a subcategory of machine learning and artificial intelligence. Instead of using explicit rules to perform a desired task as in standard algorithmic approaches, machine-learning algorithms autonomously learn from data to determine the rules for the task at hand. The idea of deep learning has been around since the 1950s but was for a long time limited by available computational power and amount of training data. Once overcome these problems, in recent years, deep learning has made great advances in solving various problems.
In this thesis, I show how deep learning can be applied in image analysis and medical diagnosis, while outperforming standard algorithmic methods and simpler machine-learning methods. I begin with showing that a convolutional neural network trained with simulated particle images is able to track experimental single particles, even in poor illumination conditions. I then show how this inspired the development of an all-in-one software to design, train and validate deep-learning solutions for digital microscopy, from particle tracking and characterization in 2D and 3D to the segmentation, characterization and counting of biological cells and image transformation. I show that this software package can be further used to develop a generative adversarial neural network to virtually stain brightfield images of cells, replacing the traditional chemical staining for a downstream analysis of biological features. I then move on from applications in microscopy and image analysis to show the potential of deep learning in medical diagnosis. I show that dense neural networks perform better than simpler machine-learning algorithm and the clinical standard in the diagnosis of a genetic disease and in the prediction of short- and long-term morbidity in patients with congenital-heart-disease. At last, I have shown that a neural network- powered strategy for testing and isolating individuals adapts to the parameters of a disease outbreak achieves an epidemic containment.
The interdisciplinary nature of the work in this thesis has allowed the application of new technologies developed in the field of physics to solve problems in the fields of biology and biomedicine, as well as overcoming barriers for the continued revolutionization of deep learning in microscopy.
Olle Fager defended his Master thesis in MPCAS at the Chalmers University of Technology on 15 June 2021. Congrats!
Title: Real-Time Multi-Object Tracking and Segmentation with Generated Data using 3D-modelling
Multi-Object Tracking and Segmentation (MOTS) is an important branch of computer vision that has applications in many different areas. In recent developments these methods have been able to reach favorable speed-accuracy trade-offs, making them interesting for real-time applications. In this work different deep learning based MOTS methods have been investigated with the purpose of extending the DeepTrack framework with real-time MOTS capabilities. Deep learning methods rely heavily on the data on which they are trained. The collection and annotation of the data can however be very time-consuming. Therefor, a pipeline is developed and investigated that automatically produces synthetic data by utilizing 3D-modelling. The most accurate tracker achieves a MOTSA score of 94 and the tracker with the best speed-accuracy trade-off achieves a MOTSA score of 88. It is also observed that satisfactory results can be achieved in most situations with a quite general data generation pipeline, indicating that the developed pipeline could be used in different scenarios.
Name of the master programme: MPCAS – Complex Adaptive Systems Supervisor: Giovanni Volpe Examiner: Giovanni Volpe, Department of Physics, University of Gothenburg Opponent: Arianit Zeqiri and Morad Mahmoudyan
Aykut Argun defended his Ph.D. thesis on June 14, 2021, at 2 pm CEST. Congrats!
The details of the presentation can be found below. The link to the webinar is announced on the faculty website.
Title: Thermodynamics of microscopic environments: From anomalous diffusion to heat engines.
Unlike their macroscopic counterparts, microscopic systems do not evolve deterministically due to the thermal noise becoming prominent. Such systems are subject to fluctuations that can only be studied within the framework of stochastic thermodynamics. Within the last few decades, the development of stochastic thermodynamics has lead to microscopic heat engines, nonequilibrium relations and the study of anomalous diffusion and active Brownian motion. In this thesis, I experimentally show that the non-Boltzmann statistics emerge in systems that are coupled to an active bath. These non-Boltzmann statistics that result from correlated active noise also disturb the nonequilibrium relations. Nevertheless, I show that these relations can be recovered using an effective potential approach. Next, I demonstrate an experimental realization of a microscopic heat engine. This engine is referred to as the Brownian gyrator, which is coupled to two different heat baths along perpendicular directions. I show that when confined into an elliptical trap that is not aligned with the temperature anisotropy, the Brownian particle is subject to a torque due to the symmetry breaking. This torque creates an autonomous engine whose direction and amplitude can be controlled by tuning the alignment of the elliptical trap. Then, I show that the force fields acting on Brownian particles can be calibrated using a data-driven method that outperforms the existing calibration methods. More importantly, I show that this method, named DeepCalib, can calibrate non-conservative and time-varying force fields that no standard calibration methods exist. Finally, I show that a similar machine-learning-based approach can be used to characterize anomalous diffusion from single trajectories. This method, named RANDI, is very versatile and performs very well in various tasks including classification, inference and segmentation of anomalous diffusion. The work presented in this thesis presents novel experiments that advance microscopic thermodynamics as well as newly developed methods that open up new possibilities in analyzing stochastic trajectories. These findings increased the scientific knowledge at the nexus between microscopic thermodynamics, anomalous diffusion, active matter and machine learning.
Supervisor: Giovanni Volpe Co-supervisors: Joakim Stenhammar, Mattias Goksör Examiner: Bernhard Mehlig Opponent: Juan M. R. Parrondo Committee: Monika Ritsch-Marte, Sabine H. L. Klapp, Édgar Roldán
Screenshots from Aykut Argun’s PhD Thesis defense.
Job assignments: Possibility 1: Work at the development of DeepTrack 2.0 (https://aip.scitation.org/doi/10.1063/5.0034891 and https://github.com/softmatterlab/DeepTrack-2.0) in close cooperation with the existing developers’ team at Soft Matter Lab. DeepTrack 2.0 is a software framework we are developing to perform quantitative digital video microscopy using deep learning. The PhD student will actively contribute to the technical design and implementation of the components of DeepTrack. Precisely, the task involves the development, testing and application of optical simulation pipelines for training deep learning networks, as well as the development of collaborative projects with other groups interested in using DeepTrack. Possibility 2: Build and operate small robots to study swarm robotics with embedded intelligence. The task involves all stages of design, fabrication, testing and programming the robots for different experiments. The robot models (inspired by the Kilobots https://ssr.seas.harvard.edu/kilobots) are small programmable units capable of motion, short-range communications, neighbor detection and more.
Appointment procedure Please apply online
The application shall include:
– Cover letter with an explanation of why you apply for the position
– CV including scientific publications
– Copy of exam certificate
– Two referees (name, telephone number, relation)
For the required Qualifications, Eligibility, Assessment criteria, Employment, see the link to the announcement below.
Agaton Fransson defended his Master thesis in MPCAS at the Chalmers University of Technology on 4 June 2021. Congrats!
Title: Tracking plankton using neural networks trained on simulated images
Softwares to track particles often use algorithmic approaches to detect particles and to create tracks using the found positions, requiring human fine-tuning of parameters to achieve sought-for results. This can be time consuming and difficult, while also creating opportunities for human error and bias. With the developments of computational power and machine learning techniques such as deep learning, data driven approaches have made their way into many fields of science. Barriers preventing advances of such methods are the lack of available training data within a field and the level of proficiency required to create custom machine learning solutions. DeepTrack 2.0 is a software providing us with means to simulate digital microscopy images, build and train neural networks such as U-nets. In this paper DeepTrack 2.0 is utilized and built on to fit the needs of marine biologists when tracking plankton. Here I show that DeepTrack 2.0 provides us with the tools necessary to detect and track different types of plankton filmed in a variety of conditions with performance on par with and with the potential to outperform conventional tracking softwares. I also show that for plankton in a messy environment moving uniformly a network trained to detect motion rather than a shape proves more successful. These results demonstrate the versatility of deep learning methods and the potential of training networks on simulations for applications on real data, as is the case for marine biologists studying plankton. They also show the impact the structure of the training data has on the nature of the network.
Name of the master programme: MPCAS – Complex Adaptive Systems Supervisor: Giovanni Volpe, Daniel Midtvedt Examiner: Giovanni Volpe, Department of Physics, University of Gothenburg Opponent: Kevin Rylander
Improving epidemic testing and containment strategies using machine learning. Laura Natali, Saga Helgadottir, Onofrio M. Maragò, Giovanni Volpe. Submitted to SDS2020 Date: 11 June Time: 16:15 (CEST)
Containment of epidemic outbreaks entails great societal and economic costs. Cost-effective containment strategies rely on efficiently identifying infected individuals, making the best possible use of the available testing resources. Therefore, quickly identifying the optimal testing strategy is of critical importance. Here, we demonstrate that machine learning can be used to identify which individuals are most beneficial to test, automatically and dynamically adapting the testing strategy to the characteristics of the disease outbreak. Specifically, we simulate an outbreak using the archetypal susceptible-infectious-recovered (SIR) model and we use data about the first confirmed cases to train a neural network that learns to make predictions about the rest of the population. Using these prediction, we manage to contain the outbreak more effectively and more quickly than with standard approaches. Furthermore, we demonstrate how this method can be used also when there is a possibility of reinfection (SIRS model) to efficiently eradicate an endemic disease.
Kevin Andersson and Eric Lindgren defended their Master thesis on 2 June 2021. Congrats!
Title: Saliency mapping of RS-fMRI data in GCNs for sex and brain age prediction
Subtitle: Identifying important functional brain networks using explainability in Graph Convolutional Networks
Insights into how biological sex and healthy ageing affects the human brain are important for an increased understanding of the brain. Healthy ageing insights are also useful for clinical applications, for instance in identifying unhealthy ageing due to neurodegenerative disease. To this end, several studies in the last few years have used machine learning methods on neuroscientific data to predict subject sex and brain age. One particularly interesting approach has been to represent functionally connected networks in the brain as graphs, and apply Graph Convolutional Networks (GCNs). To investigate which functional brain networks are connected with sex and age, we develop and analyse GCN-based models that predicts sex and age from resting-state fMRI data. The analysis of the models is done using saliency mapping techniques which gives insight into what functional brain networks in the data are relevant for the predictions. With this approach, we obtain a sex prediction accuracy of up to 79% and an age prediction MAE of 5.9 years. Furthermore, we find indications that the Sensory Motor Network and the cerebellum are among the more important functional brain networks for predicting sex and brain age.
Master programme: Physics Supervisor: Alice Neimante Diemante (Syntronic AB) Examiner: Giovanni Volpe, Department of Physics, University of Gothenburg Opponent: Rasmus Svensson